Extreme Value Characteristics of Distributions of Cumulative Processes

The paper introduces the concept of a cumulative stochastic process and derives the general mathematical expression of the distribution corresponding to such processes when they can be assumed to be Markovian. The behaviour of such a distribution in correspondence to accumulation functions of the type u(t) = atb and u(t) = l ln(l + t) is explored. It is shown how the exponential, Weibull, gamma, normal and lognormal distributions are particular cases of the general distribution. Next, the characteristics of the extreme values of n independent observations coming from such a general distribution are investigated. The central characteristics of the extreme values distributions are related to the hazard rate of the initial distribution. In particular, a simple method for relating the modal smallest value and the modal largest value to the sample size using the asymptotic expression of the hazard rate is given. The tail characteristics of the extreme values distributions are investigated numerically or analytically. The mathematical findings are applied to the volume effect on the failure probability of materials.